A privacy-preserving hybrid middleware architecture that enforces a local trust boundary as its primary design constraint that is model-agnostic, requires no retraining of the underlying LLM, and is compatible with black-box API deployments is proposed and evaluated.
This study presents a detailed, actionable approach to constructing secure, privacy-focused RAG systems and culminates in the Integrated Privacy-Preserving RAG Framework (IPRAG), a five-tier architecture supported by a three-phase deployment protocol.
A privacy-preserving zk-SNARK-based audit framework that searches for probes designed in the spirit of adversarial examples to amplify logit drift between an approved model and a modified deployment and demonstrates that token-based probes consistently deliver the strongest mean sensitivity across models and GPU platfo...
Recommendations for the design and deployment of secure LLM-enabled XR systems, emphasizing strategies such as security defense in depth, privacy-aware data practices, layered technical safeguards, continuous monitoring, and governance mechanisms that align security controls with the specific risks of immersive and int...
A data-centric security framework is proposed that links data sensitivity, access policies, model controls, and governance requirements throughout the AI lifecycle and emphasizes that security controls should remain tied to sensitive information regardless of where the data is stored, processed, retrieved, or generated...
A layered system model and an analysis pipeline that uses prior history as the temporal baseline, applies cumulative pruning and cross-proof propagation to each proof's Commitment Set, and recursively traces the survivors through historical hidden-state transitions to derive the final transaction-level Anonymity Set Si...
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